Jacob Fox is a Professor at Stanford University, specializing in Combinatorics and Probability. His research focuses on extremal combinatorics, Ramsey theory, graph theory, and additive combinatorics. He advises students like Maya Sankar. His work explores structural and enumerative aspects of graphs, hypergraphs, and combinatorial configurations. Recent studies include advancements in Ramsey numbers, sumset theory, and probabilistic methods in discrete mathematics. Key research areas include Ramsey numbers for sparse structures, hypergraph properties, and applications of combinatorial geometry. His publications often bridge theoretical insights with algorithmic applications. No scientific awards are listed in the provided text. His advising includes Maya Sankar, with research aligned to combinatorial problems. Collaborative projects involve extremal graph theory and probabilistic combinatorics. No labs or dedicated research groups are explicitly mentioned.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Daniela Iorio is an Associate Professor at the Department of Economics, University of Bologna, and holds a part-time professorship at the European University Institute’s School of Transnational Governance. She specializes in Political Economy, Health Economics, and Public Economics. Her work addresses topics like voting behavior, eating disorders, reproductive health, and parental investments. She earned a Ph.D. in Economics from the University of Pennsylvania and has held positions at institutions including USC, Universitat Autònoma de Barcelona, and the Barcelona Graduate School of Economics. Her research focuses on health policy, behavioral economics, and political institutions. Notable contributions include analyzing the link between eating disorders and addictive behaviors, evaluating IVF success disparities, and developing parenting measurement tools through digital interventions. She has been awarded the 2016 Oliver E. Williamson Prize and the Rita Levi Montalcini grant. Iorio teaches advanced courses on political economy and health economics at the PhD and master’s levels. She directs the Health Economics, Management and Policy master’s program at Bologna and has advised over a dozen graduate students. Her grants include a €758k MIUR project on child development and parental engagement. She serves as an Associate Editor for the European Economic Review and referees for top journals like the American Economic Review .
Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.
Angela Capel Cuevas is an Assistant Professor of Quantum Information Theory/Theoretical Quantum Computation at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP). Previously, she held a Junior Professorship at Eberhard Karls Universität Tübingen (2021-2024) and was an MCQST Distinguished PostDoc at TU München (2020-2021). Her research focuses on the intersection of quantum information theory and quantum many-body systems, particularly studying thermalization dynamics via quantum functional inequalities. She is a recipient of the Simons Emmy Noether Fellowship and Forbes 30 Under 30 (Spain 2023). Education: PhD in Mathematics, Universidad Autónoma de Madrid/ICMAT (2015-2019) Master in Computational Engineering and Mathematics, URV/UOC (2016-2018) Bachelor in Mathematics, Universidad de Granada (2009-2014) Research: Angela's work applies analytic and geometric tools to quantum systems, emphasizing decay of correlations in Gibbs states and quantum dissipative evolutions. Key topics include entropy inequalities, modified logarithmic Sobolev inequalities, and thermalization rates in spin chains. Her recent work bridges quantum functional analysis with practical implications for quantum computing and thermalization phenomena. Grants & Roles: PI of CRC TRR 352 'Mathematics of Many-Body Quantum Systems' (7M€) Co-PI of QuantERA project TouQan (1.25M€) Organized workshops at BIRS and Tübingen (2023-2024) Awards: Simons Emmy Noether Fellowship (2023) Forbes 30 Under 30 (2023) Vicent Caselles RSME-FBBVA Award (2022) Labs/Teams: Leads a group studying quantum dissipative systems and thermalization at DAMTP. Collaborates with institutions globally on quantum functional inequalities and Gibbs state properties.
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at the University of Chicago Booth School of Business and a Professor of Economics and the Peter de Florez Professor of EECS at the Massachusetts Institute of Technology . His work bridges machine learning , behavioral science , and computational medicine , focusing on social problems like discrimination , poverty , and health equity . Research Interests : Behavioral economics, algorithmic fairness, poverty, AI in healthcare, and policy evaluation. Teaching : Courses on Artificial Intelligence and Algorithmic Solutions to Human Problems. Publications : Over 150 papers in journals like Science , Quarterly Journal of Economics , and Nature Medicine , with recent work on AI-driven healthcare disparities and behavioral economics. Scientific Awards : MacArthur ‘Genius’ Grant, Infosys Prize, ‘Top 100 Thinker’ (Foreign Policy Magazine), ‘Young Global Leader’ (World Economic Forum). Organizations : Co-founder of ideas42 (behavioral science non-profit), J-PAL (randomized trials in development), and Dandelion Health (healthcare data for AI). Serves on the MacArthur Foundation board.
Haitong Li is an Assistant Professor in the School of Electrical and Computer Engineering at Purdue University's College of Engineering, joining the faculty in 2022. His research bridges nanoelectronic devices, integrated circuits, and nanotechnology-inspired AI hardware to address critical challenges in energy-efficient artificial intelligence systems. Education: Ph.D. in Electrical Engineering, Stanford University Research Interests: Dr. Li pioneers emerging memory technologies—particularly Resistive RAM (RRAM)—for in-memory computing and neuromorphic systems. His work focuses on 3D monolithic integration of RRAM and gain cell memory with CMOS to enable edge AI, with recent breakthroughs in hardware acceleration for large language models and sustainable computing. Key innovations include carbon footprint prediction for LLMs and zeroth-order fine-tuning techniques. Publication Trends: Dr. Li's 2023-2025 publications reveal a strategic shift toward sustainable AI hardware, emphasizing carbon-aware LLM inference and edge deployment. His research consistently targets data movement reduction through memory-centric architectures, spanning photonic accelerators, neuro-symbolic computing, and heterogeneous 3D integration. Awards: No scientific awards were documented in the provided sources. Advising and Grants: Current advisees and grant funding details were not specified in the available materials. Labs and Teams: Research group composition and laboratory facilities were not described in the source text.
Allison Ross Eckard, MD is Professor in the Departments of Pediatrics and Medicine at the Medical University of South Carolina (MUSC) College of Medicine, serving as Division Chief for Pediatric Infectious Diseases and Director of MUSC's Ryan White Pediatric HIV Clinic and HIV Transition Clinic. She maintains an adjunct faculty position at Emory University and holds board certification in Pediatrics and Pediatric Infectious Diseases. Her primary research focuses on metabolic, cardiovascular, and nutritional complications of chronic HIV and antiretroviral therapy across age groups, with emphasis on inflammation's role in comorbidities like cardiovascular disease and bone disorders. Current investigations include NIH R01-funded research on interventions for weight gain and abdominal fat in HIV, vitamin D supplementation, and statin therapy. She has published nearly 60 peer-reviewed papers on HIV pathogenesis and treatment optimization. Dr. Eckard co-chairs the DHHS panel for Guidelines for the Prevention and Treatment of Opportunistic Infections in HIV-Exposed and HIV-Infected Children and serves on the DHHS panel for Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents with HIV . Her clinical work specializes in comprehensive HIV care for children through young adults, with particular focus on adolescent/young adult transition issues and global health initiatives through Lespwa Timoun in Haiti. Recent efforts include developing COVID-19 treatment guidelines for children and school safety protocols. HIV Specialist certification (American Academy of HIV Medicine) Fellow, Infectious Diseases Society of America Fellow, Pediatric Infectious Diseases Society Fellow, HIV Medical Association Member, HIV Medical Association's 'Ending the HIV Epidemic' working group She leads MUSC's HIV education initiatives and serves as inpatient consultant for pediatric infectious diseases, with advanced training in tropical medicine. Her NIH-funded research examines targeted interventions to minimize comorbidity development in HIV, while her clinical leadership spans the Ryan White HIV program, transition clinics, and global health partnerships.
Annina Iseli is a Lecturer in the Department of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Basic Sciences. She holds dual roles as both a Scientist and Lecturer, contributing to research and teaching within the Institute of Mathematics (MATH-GE). Her work bridges pure and applied mathematics, focusing on geometric and analytical properties of fractals. University: École Polytechnique Fédérale de Lausanne (EPFL) School: College of Basic Sciences Department: Institute of Mathematics (MATH-GE) Role: Lecturer and Scientist Research interests span multiple areas in mathematics: Fractal geometry and its connections to complex dynamics Geometric measure theory applications to projections in normed spaces Conformal and quasiconformal geometry Analysis in metric spaces Her publications include work on projection theorems, fractal dimensions, and hyperbolic space properties. She co-organizes the Quasiworld Seminar and EPFL Geometry Seminar, collaborating with researchers like Mario Bonk, Nicolas Monod, and Zoltán Balogh. Contact: annina.iseli@epfl.ch
Jonathan W. Friedberg, M.D., M.M.Sc. is the Director of the Wilmot Cancer Institute and Professor in the Department of Medicine, Hematology/Oncology at the University of Rochester School of Medicine and Dentistry. He holds the Samuel E. Durand Chair in Medicine and leads one of the nation's premier lymphoma programs. Dr. Friedberg is internationally recognized for his expertise in lymphoma treatment, particularly in developing novel therapies and clinical trial approaches. Dr. Friedberg's research focuses on lymphoma, with particular expertise in Hodgkin lymphoma, non-Hodgkin lymphoma, Waldenstrom macroglobulinemia, and chronic lymphocytic leukemia. His work spans the entire treatment continuum from diagnosis through novel therapies including autologous stem cell transplantation and CAR-T cell interventions. He has built a comprehensive lymphoma program with expertise spanning hematopathology, radiation oncology, dermatology, and neurology, in addition to hematology and medical oncology. His research interests include developing risk-adapted treatment strategies, investigating novel therapeutic agents, and improving outcomes for patients with various lymphoma subtypes. Dr. Friedberg's extensive publication record demonstrates his leadership in lymphoma research, with recent work focusing on immunotherapy combinations, predictive modeling, risk stratification, and novel treatment approaches for various lymphoma subtypes. His research has significantly contributed to the understanding and treatment of lymphomas, particularly in developing more personalized and effective treatment strategies. Scientific Awards: Faculty Academic Mentoring Award (2012) Scholar in Clinical Research (2008) America's Top Doctors Selection (2008) Jacob Gitelman Award (2007) Lawrence A. Kohn Senior Teaching Fellow (2004-2006) Clinical Investigator Career Development Award (2003) Clinical Oncology Research Fellowship "Immunotherapy of Hodgkin's Disease" (2001-2003) Rising stars program for innovative research (2001-2003) As Director of the Wilmot Cancer Institute, Dr. Friedberg leads numerous clinical trials and research initiatives focused on advancing lymphoma treatment. He is actively involved in SWOG and has served in leadership roles for multiple clinical trials investigating novel therapies for lymphoma patients. His practice team includes Anna Morrison, R.N., and Kerri Hugelmaier, N.P., providing comprehensive care for lymphoma patients. Dr. Friedberg has established the Lymphoma Epidemiology of Outcomes (LEO) Consortium, a large observational cohort study supporting broad research on NHL prognosis and survivorship. His leadership extends to national organizations where he contributes to developing clinical practice guidelines and consensus recommendations for lymphoma treatment.
Ji Zhu is the Susan A. Murphy Collegiate Professor of Statistics at the University of Michigan, Department of Statistics. He holds affiliations with the Michigan Institute for Data Science (MIDAS) and the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP). His research focuses on statistical machine learning, network analysis, and health science applications. Education: B.Sc. in Physics (Peking University, 1996), M.Sc. and Ph.D. in Statistics (Stanford University, 2000 and 2003). Notable awards include the NSF CAREER Award (2008), Fellowships from the ASA (2013) and IMS (2015), and recognition as a Web of Science Highly Cited Researcher (2014–2020). Research interests span statistical methodologies for networks, survival analysis, and high-dimensional data. He co-authored influential papers on community detection, network cross-validation, and latent space models. Current editorial roles include Editor-in-Chief of the Annals of Applied Statistics and Action Editor for the Journal of Machine Learning Research. Advising: Supervised over 50 students and postdocs, many now in academia and industry. Notable former advisees include Tianxi Li (University of Minnesota), Yuan Zhang (Ohio State University), and Weijing Tang (Carnegie Mellon University). Labs/Teams: Active in interdisciplinary projects at MIDAS and MiCHAMP, focusing on healthcare analytics and predictive modeling for diseases like hepatitis and cardiovascular outcomes.
Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.
